Docker Env Manager

Docker Environment Manager (Tool Layer)

Overview

Docker is the backbone for reproducible, containerized, GPU-accelerated environments in NeuroClaw — especially for deep-learning and neuroimaging skills (MARS-WMH nnU-Net, future nnU-Net models, containerized FreeSurfer, etc.) that require exact runtime isolation, NVIDIA GPU passthrough, and large pre-built images.

This skill acts as the interface-layer orchestrator for all common Docker operations, preventing permission issues, GPU misconfiguration, port conflicts, and storage bloat while enforcing best practices (named containers, volume mounts, --gpus all, docker-compose for multi-service stacks, dry-run previews, and safe pruning).

Strict workflow (never skipped):

1. Parse user intent from the request or context (pull / run / build / compose / prune / list / cleanup).
2. Detect current Docker setup (docker --version, docker info, NVIDIA Container Toolkit via nvidia-smi through claw-shell, available disk space, GPU status).
3. Propose a safe, best-practice plan:
   • Always prefer named containers/volumes over anonymous ones
   • Suggest --gpus all + volume mounts for NeuroClaw GPU skills
   • Recommend docker-compose.yml for reproducible multi-container stacks
   • Use --dry-run equivalents and plan preview by default
   • Warn about large image pulls (several GB), permission issues (chmod -R 777 on data dirs), and GPU driver mismatches
   • Route all actual docker run/pull/build commands through claw-shell
4. Show numbered plan + exact commands + estimated time/size + risks.
5. Wait for explicit user confirmation (“YES”, “execute”, “proceed”).
6. On approval: delegate execution safely to claw-shell (with logging), capture output, report success/failure, and suggest next steps.

Core safety & best-practice rules

• Never run destructive commands (docker system prune -a, docker rm -f) without double confirmation
• All shell-level Docker commands must go through claw-shell (centralized logging + safety gate)
• Prefer docker compose over legacy docker-compose
• Integrate with dependency-planner for installing Docker + NVIDIA Container Toolkit
• Log all actions to ./logs/docker_YYYYMMDD_HHMMSS.log

Quick Reference (Common NeuroClaw Tasks)

Installation

This skill is pure Python orchestration — no external binaries needed beyond a working Docker installation.

Required prerequisites (must exist before activation):

• Working Docker Engine (with NVIDIA Container Toolkit for GPU skills)
• claw-shell (mandatory — all Docker commands are routed here)
• dependency-planner (recommended companion for installing Docker + NVIDIA Container Toolkit)
• Basic shell access

To register in NeuroClaw:

# Place files in: skills/docker-env-manager/
# Update SOUL.md and/or USER.md with trigger phrases

NeuroClaw recommended wrapper script

# docker_env_manager.py
import subprocess
import argparse
import sys
from pathlib import Path
from datetime import datetime

def run_docker_cmd(cmd, dry_run=False):
    full_cmd = ["docker"] + cmd
    print("Would run (via claw-shell):", " ".join(full_cmd))
    # In real implementation: delegate to claw-shell skill after user confirmation
    # e.g. tool_call("claw_shell_run", {"command": " ".join(full_cmd)})

def get_docker_info():
    try:
        version = subprocess.check_output(["docker", "--version"]).decode().strip()
        info = subprocess.check_output(["docker", "info", "--format", "{{.ServerVersion}}"]).decode().strip()
        return f"Docker {version} | Engine {info}"
    except:
        return "Docker not found or error."

def check_gpu():
    try:
        return subprocess.check_output(["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"]).decode().strip()
    except:
        return "No NVIDIA GPU detected or NVIDIA Container Toolkit missing"

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="NeuroClaw Docker Environment Manager")
    parser.add_argument("--request", required=True, help="User request (pull/run/compose/prune...)")
    parser.add_argument("--dry-run", action="store_true", help="Only show plan")
    args = parser.parse_args()

Harness-Aware Container Isolation & Security Standards

All Docker operations performed via docker-env-manager must enforce strict isolation, security boundaries, and reproducibility standards.

Container Specification Format (Declarative Registry)

Each containerized skill must include a Docker specification manifest:

File: skills/{skill_name}/docker-spec.json

{
  "skill_name": "wmh-segmentation",
  "container_name": "neuroclaw-wmh-seg",
  "image": "ghcr.io/miac-research/wmh-nnunet:latest",
  "image_hash": "sha256:abc123...",
  "container_security": {
    "read_only_root": true,
    "cap_drop": ["ALL"],
    "cap_add": ["NET_BIND_SERVICE"],
    "security_opts": ["no-new-privileges:true"],
    "user": "1000:1000",
    "privileged": false
  },
  "resource_limits": {
    "memory_limit": "16G",
    "memory_swap": "16G",
    "cpus": "4",
    "pids_limit": 512,
    "ulimits": {
      "nofile": 1024,
      "nproc": 512
    }
  },
  "network_config": {
    "network": "bridge",
    "expose_ports": [],
    "environment_whitelist": [
      "CUDA_VISIBLE_DEVICES",
      "TORCH_HOME"
    ]
  },
  "volume_mounts": {
    "/data": {
      "bind": "{data_path}",
      "mode": "ro",
      "required": true
    },
    "/output": {
      "bind": "{output_path}",
      "mode": "rw",
      "required": true
    },
    "/tmp": {
      "bind": "/dev/shm",
      "mode": "rw",
      "size_limit": "8G"
    }
  },
  "gpu_config": {
    "enabled": true,
    "device_ids": "all",
    "driver_capabilities": "compute,utility"
  },
  "logging": {
    "driver": "json-file",
    "options": {
      "max-size": "10m",
      "max-file": "3"
    }
  }
}

Container Execution Protocol

Step 1: Pre-execution container validation

# Verify image integrity
docker inspect ${image_hash} --format='{{.RepoDigests}}'

# Check for vulnerabilities (if vulnerability scanner available)
trivy image --severity HIGH,CRITICAL ${image}

# Verify GPU access
nvidia-smi -L  # List all GPUs

# Pre-flight resource check
docker stats --no-stream ${existing_container_name} || true

Step 2: Secure container creation Template (auto-generated based on docker-spec.json):

docker run \
  --name ${CONTAINER_NAME} \
  --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges:true \
  --user 1000:1000 \
  --memory=${MEMORY_LIMIT} \
  --memory-swap=${MEMORY_LIMIT} \
  --cpus=${CPU_LIMIT} \
  --pids-limit=512 \
  --gpus=all \
  --env CUDA_VISIBLE_DEVICES=${GPU_DEVICES} \
  -v ${DATA_PATH}:/data:ro \
  -v ${OUTPUT_PATH}:/output:rw \
  -v /dev/shm:/tmp:rw \
  --log-driver=json-file \
  --log-opt max-size=10m \
  --log-opt max-file=3 \
  --network=bridge \
  ${IMAGE} \
  ${COMMAND}

Step 3: Post-execution audit logging

{
  "execution_time": "2026-04-05T14:32:00Z",
  "container_id": "abc123def456",
  "image": "ghcr.io/miac-research/wmh-nnunet:latest",
  "command": "python inference.py --input /data --output /output",
  "exit_code": 0,
  "resource_usage": {
    "memory_peak_mb": 8192,
    "cpu_time_seconds": 1203.5,
    "gpu_memory_peak_mb": 6144,
    "io_read_bytes": 5368709120,
    "io_write_bytes": 2684354560
  },
  "security_events": [],
  "status": "SUCCESS"
}

Data Privacy & Isolation Guardrails

Mandatory privacy checks before container execution:

1. Input data anonymization verification:

   • Scan for known PII patterns (patient IDs, medical record numbers, names, birthdates)
   • Flag any unredacted identifiable information
   • Hash-verify input against reference dataset (prevent contamination)

2. Output post-processing:

   • Scan generated files for embedded PII or metadata
   • Automatically redact timestamps, user paths from logs
   • Generate sanitized output copies for sharing

3. Filesystem sandboxing audit:

   • Verify no mounts point to system directories (/, /etc, /root, /home)
   • Enforce read-only root filesystem
   • Whitelist environment variables (drop all except approved list)

Generated audit report (container_privacy_audit.json):

{
  "container": "wmh-seg-20260405",
  "privacy_scan": {
    "input_files_scanned": 50,
    "pii_detected": 0,
    "output_files_sanitized": 50,
    "suspicious_environment_vars": 0,
    "mount_violations": 0,
    "status": "CLEARED"
  },
  "isolation_verification": {
    "readonly_root": true,
    "privilege_dropping": true,
    "network_isolation": true,
    "capability_dropping": true
  }
}

Container Reproducibility & Layer Verification

Image integrity checking (SHA256 verification at run time):

# Pull image and verify digest
EXPECTED_DIGEST="sha256:abc123..."
docker pull ${IMAGE}
ACTUAL_DIGEST=$(docker inspect ${IMAGE} --format='{{index .RepoDigests 0}}' | cut -d'@' -f2)

if [ "$EXPECTED_DIGEST" != "$ACTUAL_DIGEST" ]; then
  echo "ERROR: Image digest mismatch. Potential supply chain attack."
  exit 1
fi

Layer-by-layer audit trail (container_layer_manifest.json):

{
  "image": "ghcr.io/miac-research/wmh-nnunet:latest",
  "total_layers": 12,
  "base_image": "nvidia/cuda:12.1-cudnn8-runtime-ubuntu22.04",
  "layers": [
    {
      "layer_index": 0,
      "digest_sha256": "def456...",
      "size_bytes": 1234567,
      "instruction": "FROM nvidia/cuda:12.1-cudnn8-runtime-ubuntu22.04"
    },
    {
      "layer_index": 1,
      "digest_sha256": "ghi789...",
      "size_bytes": 567890,
      "instruction": "RUN apt-get update && apt-get install -y python3-dev..."
    }
  ],
  "final_image_hash": "sha256:abc123...",
  "verification_timestamp": "2026-04-05T14:20:00Z"
}

Checkpoint & Resume with Container State

Long-running containerized tasks support state persistence:

# 1. Create checkpoint from running container
docker checkpoint create ${CONTAINER_ID} checkpoint_20260405_143000

# 2. Auto-saved checkpoint metadata
docker checkpoint ls ${CONTAINER_ID}  # Lists all available checkpoints

# 3. Resume from checkpoint (if host supports CRIU)
docker start --checkpoint checkpoint_20260405_143000 ${CONTAINER_ID}

Important Notes & Limitations

• User confirmation is mandatory — no silent destructive actions
• All actual Docker commands are delegated to claw-shell for centralized logging and safety
• Large image pulls (e.g. nnU-Net models) → warn about size/time and disk space
• Common fixes (chmod -R 777 on mounted directories, newgrp docker) are automatically included in plans
• Windows users are encouraged to use WSL2 + Docker Desktop for best compatibility
• GPU passthrough requires NVIDIA Container Toolkit (installed via dependency-planner)
• Rollback support: keep docker-compose.yml + image tags as backups

When to Call This Skill

• Any request containing “docker”, “container”, “pull image”, “run with GPU”, “docker compose”, “prune”, “build Dockerfile”
• Before running Docker-based skills (wmh-segmentation, future containerized models)
• When dependency-planner detects missing Docker/NVIDIA toolkit
• Reproducibility, sharing, or team collaboration tasks involving containers

Complementary / Related Skills

• claw-shell → mandatory safe execution of all Docker commands (tmux claw session)
• dependency-planner → install Docker Engine + NVIDIA Container Toolkit
• multi-search-engine → lookup latest Docker Hub / GitHub Container Registry instructions

Reference

Custom interface-layer skill for NeuroClaw, addressing containerized environment setup gaps identified in the MedicalClaw / OpenClaw evaluation and aligned with 2026 Docker + NVIDIA best practices (GPU passthrough, compose reproducibility, claw-shell routing).

Created At: 2026-03-25 23:08 HKT
Last Updated At: 2026-04-05 02:01 HKT Author: chengwang96